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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Additional noise data on the SR-3 propeller

The noise generated by supersonic-tip-speed propellers is investigated. An eight bladed propeller was tested in the Lewis 8- by 6-foot wind tunnel with conditions providing data in the subsonic operating region of the propeller. These conditions resulted in a slight reshaping of the curve for blade passing tone as a function of helical tip Mach number as compared with previous results. Directivity curves with an additional transducer position gave an indication of a lobe pattern for this propeller that was not previously observed. The present data at the aft-most position indicate that some reflections, possibly from the test rig support strut, may have affected the data taken previously.

Dittmar, J. H.↗
DOE OSTI2025

Block Island Noise Modeling Data

Noise propagation near Block Island was simulated to assess environmental impacts of impact pile driving during wind turbine construction. Computational models complement field-recorded acoustic data, providing insights into sound attenuation, spectral variability, and propagation dynamics. The dataset includes: 1. Propagation Models: Simulated underwater sound fields documenting sound pressure and directional variability across frequency bands and distances. 2. Spectral Analysis (LTSA): Long-term averages and processed outputs calculating acoustic intensity over time. 3. Visualization Files: Graphs, 2D/3D simulation results, and reference calculations used in sound modeling.

17 WIND ENERGY↗
DOE OSTI2026

Fermilab PIP II machine protection system digitized data noise elimination scheme and its FPGA implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, J. [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2025

Fermilab PIP II Machine Protection System Digitized Data Noise Elimination Scheme and Its FPGA Implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can con-taminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is re-quired. Several real-time functional blocks were simu-lated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid base-line by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗
NASA NTRS1990

The prediction of STOVL noise - Current semiempirical methods and comparisons with jet noise data

The prediction of conventional or STOVL turbojet propulsion system-using aircraft noise is presently undertaken by means of a method incorporating empirical models for jet-mixing noise, engine core noise, and broadband shock noise. The free-jet noise is coupled with a novel empirical equation for ground-interaction noise generated by a vertically impinging jet, and supplemented with the out-of-ground-effect free-jet acoustic directivity pattern of a Harrier-type vectoring nozzle installation. This acoustic-prediction method yielded reasonable agreement with measured far-field Harrier noise during hover in and out of ground effect. Unlike small-scale studies of jet impingement on a hard surface, no tones were found in the present Harrier nozzle spectra.

Soderman, Paul T.↗
NASA NTRS1976

Static and wind tunnel near-field/far-field jet noise measurements from model scale single-flow baseline and suppressor nozzles. Volume 1: Noise source locations and extrapolation of static free-field jet noise data

A test was conducted in the Boeing Large Anechoic Chamber to determine static jet noise source locations of six baseline and suppressor nozzle models, and establish a technique for extrapolating near field data into the far field. The test covered nozzle pressure ratios from 1.44 to 2.25 and jet velocities from 412 to 594 m/s at a total temperature of 844 K.

Jaeck, C. L.↗
DOE OSTI2025

Local-Regional Surface Wave Dispersion Analysis using Ambient Noise Data of the Southern Nevada National Security Site Area

This report details seismic ambient noise analysis to improve seismic velocity estimates of the southeastern Nevada National Security Site (NNSS). We compare two different methods for estimating surface wave dispersion curves from ambient noise cross-correlations: frequency time analysis (FTAN) and Aki’s cross-spectral method (XSpec). We find that XSpec performs better for our local-regional dataset and frequency content. Using phase velocity estimates from XSpec, we build a preliminary phase velocity dispersion dataset, which contains data for 1,054 station pairs and 8,905 discrete phase velocity measurements for periods between 0.5 and 13 s. This phase velocity dataset will be incorporated into an updated local-regional P and S wave velocity model of the southeastern NNSS in the future and is expected to improve upon shallow velocity estimates.

47 OTHER INSTRUMENTATION↗
NASA NTRS1975

Noise data from tests of a 1.83 meter (6-ft-) diameter variable-pitch 1.2-pressure-ratio fan (QF-9)

Acoustic and aerodynamic data for a 1.83-meter (6-ft.) diameter fan suitable for a quiet engine for short-takeoff-and-landing (STOL) aircraft are documented. The QF-9 rotor blades had an adjustable pitch feature which provided a means for testing at several rotor blade setting angles, including one for reverse thrust. The fan stage incorporated features for low noise. Far-field noise around the fan was measured without acoustic suppression over a range of operating conditions for six different rotor blade setting angles in the forward thrust configuration, and for one in the reverse configuration. Complete results of one-third-octave band analysis of the data are presented in tabular form. Also included are power spectra, data referred to the source, and sideline perceived noise levels.

Glaser, F. W.↗
NASA NTRS2006

Flight Acoustic Testing and For the Rotorcraft Noise Data Acquisition Model (RNM)

Two acoustic flight tests have been conducted on a remote test range at Eglin Air Force Base in the panhandle of Florida. The first was the "Acoustics Week" flight test conducted in September 2003. The second was the NASA Heavy Lift Rotorcraft Acoustics Flight Test conducted in October-November 2005. Benchmark acoustic databases were obtained for a number of rotorcraft and limited fixed wing vehicles for a variety of flight conditions. The databases are important for validation of acoustic prediction programs such as the Rotorcraft Noise Model (RNM), as well as for the development of low noise flight procedures and for environmental impact assessments. An overview of RNM capabilities and a detailed description of the RNM/ART (Acoustic Repropagation Technique) process are presented. The RNM/ART process is demonstrated using measured acoustic data for the MD600N. The RNM predictions for a level flyover speed sweep show the highest SEL noise levels on the flight track centerline occurred at the slowest vehicle speeds. At these slower speeds, broadband noise content is elevated compared to noise levels obtained at the higher speeds. A descent angle sweep shows that, in general, ground noise levels increased with increasing descent rates. Vehicle orientation in addition to vehicle position was found to significantly affect the RNM/ART creation of source noise semi-spheres for vehicles with highly directional noise characteristics and only mildly affect those with weak acoustic directionality. Based on these findings, modifications are proposed for RNM/ART to more accurately define vehicle and rotor orientation.

Burley, Casey L.↗
NASA NTRS1979

A comparison of linear acoustic theory with experimental noise data for a small-scale hovering rotor

Linear acoustic calculations based on full aerodynamic data as input are presented and compared with measured cases reported by Boxwell et al. (1978). The full aerodynamic data are obtained using three programs giving radial loading, chordwise loading, and chordwise position of transition. It is shown that in the theoretical results the most significant noise source mechanism is due to blade thickness. Thus the conclusions of Boxwell et al. as to the importance of nonlinearities around the blades are upheld. These conclusions concern the width, shape and the level of the acoustic pressure calculated from linear acoustic theory. Some of the approximations involved in the application of acoustic analogy using quadrupole sources are discussed. It is necessary that the near- and far-field problems of rotating blades be treated together as shown for the case of an oscillating sphere.

Farassat, F.↗
NASA NTRS1982

User's guide to noise data acquisition and analysis programs for HP9845: Nicolet analyzers

A software interface package was written for use with a desktop computer and two models of single channel Fast Fourier analyzers. This software features a portable measurement and analysis system with several options. Two types of interface hardware can alternately be used in conjunction with the software. Either an IEEE-488 Bus interface or a 16-bit parallel system may be used. Two types of storage medium, either tape cartridge or floppy disc can be used with the software. Five types of data may be stored, plotted, and/or printed. The data types include time histories, narrow band power spectra, and narrow band, one-third octave band, or octave band sound pressure level. The data acquisition programming includes a front panel remote control option for the FFT analyzers. Data analysis options include choice of line type and pen color for plotting.

Mcgary, M. C.↗
DOE OSTI2025

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?

Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attributed to the recent performance breakthroughs in deep learning, the growing availability of computational power, data, and easy-to-use ML libraries. However, these empirical successes have often outpaced our formal understanding of the ML algorithms. An important but under-rated area is uncertainty quantification (UQ) of ML. ML-based models are subject to approximation uncertainty when they are used to make predictions, due to sources including but not limited to, data noise, data coverage, extrapolation, imperfect model architecture and the stochastic training process. The goal of this paper is to clearly explain and illustrate the importance of UQ of ML. We will elucidate the differences in the basic concepts of UQ of physics-based models and data-driven ML models. Various sources of uncertainties in physical modeling and data-driven modeling will be discussed, demonstrated, and compared. We will also present and demonstrate a few techniques to quantify the ML prediction uncertainties, including Monte Carlo dropout, deep ensemble, Bayesian neural networks, Gaussian Processes and conformal prediction. Lastly, we will discuss the need for building a verification, validation and UQ framework to establish ML credibility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1983

Comet Halley ephemeris uncertainties in 1985-1986

For the planned flyby missions to Comet Halley in March 1986, the comet's ephemeris uncertainties completely dominate the spacecraft-comet miss distance. In an effort to determine realistic Comet Halley ephemeris uncertainties, a statistical covariance analysis was conducted using the actual data in 1909-1910-1911 and simulated data in 1984-1985-1986. In 1985-1986, Comet Halley's ephemeris uncertainties are very sensitive to the comet's orbital position, the optical data noise, data schedule, and whether or not the old data is included in the orbital solutions. The comet's ephemeris uncertainties in March 1986 are relatively insensitive to reasonable center of light/center of mass offsets and also to possible radar data taken in late November 1985. Accurate Space Telescope observations made in early March 1986 might significantly improve upon the comet's position uncertainties for the various intercepting spacecraft.

Yeomans, D. K.↗
NASA NTRS1976

Data analysis and noise prediction for the QF-1B experimental fan stage

The results of a fan noise data analysis and prediction effort using experimental data obtained from tests on the QF-1B research fan are described. Surface pressure measurements were made with flush mounted sensors installed on selected rotor blades and stator vanes and noise measurements were made by microphones located at the far field. Power spectral density analysis, time history studies, and calculation of coherence functions were made. The emphasis of these studies was on the characteristics of tones in the spectra. The amplitude behavior of spectral tones was found to have a large, often predominant, random component, suggesting that turbulent processes play an important role in the generation of tonal as well as broadband noise. Inputs from the data analysis were used in a prediction method which assumes that acoustic dipoles, produced by unsteady blade and van forces, are the important source of fan noise.

Bliss, D. B.↗
NASA NTRS1973

Noise and wake structure measurements in a subsonic tip speed fan: Tabulation and plots of test data

Noise and wake structure measurements in a ducted fan were conducted. The tip speed was kept at subsonic levels. The anechoic platform used during the test is described. The following conditions are reported: (1) one third octave band analyses of the fan noise data, (2) narrow band analyses of the fan noise for selected test conditions, (3) narrow band sound power level data for all fan test conditions, and (4) velocity and air angle evaluation of blade wake data.

Magliozzi, B.↗
NASA NTRS2002

Small Engine Technology (SET). Task 33: Airframe, Integration, and Community Noise Study

Task Order 33 had four primary objectives as follows: (1) Identify and prioritize the airframe noise reduction technologies needed to accomplish the NASA Pillar goals for business and regional aircraft. (2) Develop a model to estimate the effect of jet shear layer refraction and attenuation of internally generated source noise of a turbofan engine on the aircraft system noise. (3) Determine the effect on community noise of source noise changes of a generic turbofan engine operating from sea level to 15,000 feet. (4) Support lateral attenuation experiments conducted by NASA Langley at Wallops Island, VA, by coordinating opportunities for Contractor Aircraft to participate as a noise source during the noise measurements. Noise data and noise prediction tools, including airframe noise codes, from the NASA Advanced Subsonic Technology (AST) program were applied to assess the current status of noise reduction technologies relative to the NASA pillar goals for regional and small business jet aircraft. In addition, the noise prediction tools were applied to evaluate the effectiveness of airframe-related noise reduction concepts developed in the AST program on reducing the aircraft system noise. The AST noise data and acoustic prediction tools used in this study were furnished by NASA.

Lieber, Lys S.↗
NASA NTRS2010

Data Quality Assurance for Supersonic Jet Noise Measurements

The noise created by a supersonic aircraft is a primary concern in the design of future high-speed planes. The jet noise reduction technologies required on these aircraft will be developed using scale-models mounted to experimental jet rigs designed to simulate the exhaust gases from a full-scale jet engine. The jet noise data collected in these experiments must accurately predict the noise levels produced by the full-scale hardware in order to be a useful development tool. A methodology has been adopted at the NASA Glenn Research Center s Aero-Acoustic Propulsion Laboratory to insure the quality of the supersonic jet noise data acquired from the facility s High Flow Jet Exit Rig so that it can be used to develop future nozzle technologies that reduce supersonic jet noise. The methodology relies on mitigating extraneous noise sources, examining the impact of measurement location on the acoustic results, and investigating the facility independence of the measurements. The methodology is documented here as a basis for validating future improvements and its limitations are noted so that they do not affect the data analysis. Maintaining a high quality jet noise laboratory is an ongoing process. By carefully examining the data produced and continually following this methodology, data quality can be maintained and improved over time.

Brown, Clifford A.↗
NASA NTRS1991

Neuromorphic learning of continuous-valued mappings from noise-corrupted data

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocontroller with good noise-filtering properties. In the presence of plant noise, the neurocontroller is found to be more stable than the teacher. A novel perspective on the application of neural network technology to control engineering is presented.

Troudet, T.↗